Journal of Intelligent Manufacturing

, Volume 30, Issue 3, pp 1099–1110 | Cite as

Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing

  • M. Musselman
  • H. Xie
  • D. DjurdjanovicEmail author


Slit valves play an important role in semiconductor manufacturing, enabling creation and maintaining of a vacuum environment required for wafer processing. Due to the high volume of production in the modern semiconductor industry, slit valves could experience severe degradation over their lifetime. If maintenance is not applied in due time, degraded valves may lead to defects in finished products due to pressure loss and particle generation. In this paper, we propose methods for signal processing and feature extraction for analysis of slit valve vibration signals. These methods are then used to demonstrate the ability to reliably, accurately and efficiently distinguish between vibration patterns of each individual valve via a multi-class classification procedure. Furthermore, instantaneous time–frequency entropy of valve vibrations enabled long term monitoring of a slit valve in production, in spite of variations in valve speed and operations.


Slit valves Semiconductor manufacturing Vibrations based monitoring Nonstationary signal analysis Multi-class classification 



This research is supported in part by the National Science Foundation (NSF) grant IIP 1266279. The content of this paper is solely the responsibility of the authors and does not represent the official views of the NSF.


  1. Analog Devices. (2015). ADXL327 data sheet. Accessed 15 August 2015
  2. Bao, J., & Spanos, C. J. (2001). A simulation framework for lithography process monitoring and control using scatterometry. In AEC/APC Symposium XIII, Abstract available via Accessed 15 August 2015
  3. Cholettte, M., Celen, M., Djurdjanovic, D., & Rasberry, J. (2013). Condition monitoring and operational decision making in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 26(4), 454–464.CrossRefGoogle Scholar
  4. Coates, M., & Fitzgerald, W. (1999). Regionally optimised time–frequency distributions using finite mixture models. Signal Processing, 77(3), 247–260.CrossRefGoogle Scholar
  5. Cohen, L. (1995). Time–frequency analysis: Theory and applications (1st ed.). Englewood Cliffs: Prentice Hall.Google Scholar
  6. Djurdjanovic, D., Ni, J., & Lee, J. (2002). Time–frequency based sensor fusion in the assessment and monitoring of machine performance degradation. In Proceedings of the 2002 ASME international mechanical engineering congress and exposition (IMECE) (pp. 15–22).Google Scholar
  7. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). London: Wiley.Google Scholar
  8. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.CrossRefGoogle Scholar
  9. Facco, P., Bezzo, F., Barolo, M., Mukherjee, R., & Romagnoli, J. A. (2009). Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysis. AIChE Journal, 55(5), 1147–1160.CrossRefGoogle Scholar
  10. Flett, J., & Bone, G. M. (2016). Fault detection and diagnosis of diesel engine valve trains. Mechanical Systems and Signal Processing, 72, 316–327.CrossRefGoogle Scholar
  11. Fulton, S., & Kim, M. (2007). ISMI consensus on preventive and predictive maintenance vision Ver. 1.1. In: International SEMATECH manufacturing initiative (ISMI). Accessed 15 August 2015
  12. Guan, T., Kuang, Y. C., Ooi, M., Cheah, X. G., Tan, Y. S., & Demidenko, S. (2011). Data-driven condition-based maintenance of test handlers in semiconductor manufacturing. In Proceedings of the 6th IEEE international symposium on electronic design, test and application (DELTA) (pp. 189–194).Google Scholar
  13. Heng, R., & Nor, M. (1998). Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics, 53(1), 211–226.CrossRefGoogle Scholar
  14. Hong, S. J., Lim, W. Y., Cheong, T., & May, G. S. (2012). Fault detection and classification in plasma etch equipment for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 25(1), 83–93.CrossRefGoogle Scholar
  15. Hopfe, V., Sheel, D., Spee, C., Tell, R., Martin, P., Beil, A., et al. (2003). In-situ monitoring for CVD processes. Elsevier Journal on Thin Solid Films, 442(1), 60–65.CrossRefGoogle Scholar
  16. Kim, B., & May, G. S. (1997). Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system. IEEE Transactions on Components, Packaging, and Manufacturing Technology—Part C, 20(1), 39–47.CrossRefGoogle Scholar
  17. Jeong, J., & Williams, W. J. (1992). Kernel design for reduced interference distributions. IEEE Transactions on Signal Processing, 40(2), 402–412.CrossRefGoogle Scholar
  18. Jones, D. L., & Baraniuk, R. G. (1995). An adaptive optimal-kernel time-frequency representation. IEEE Transactions on Signal Processing, 43(10), 2361–2371.CrossRefGoogle Scholar
  19. Jong, W. R., & Lin, T.-W. (2007). Statistical process control for e-diagnostic prediction of cluster-tool equipment. In Proceedings of the 33rd annual conference of the IEEE industrial electronics society—IECON 2007 (pp. 2916–2921).Google Scholar
  20. Kittler, J. (1975). Mathematical methods of feature selection in pattern recognition. International Journal of Man-Machine Studies, 7(5), 609–637.CrossRefGoogle Scholar
  21. Kreßel, U. H.-G. (1999). Pairwise classification and support vector machines. In B. Schölkopf & C. J. C. Burges (Eds.), Advances in kernel methods (pp. 255–268). New York: MIT Press.Google Scholar
  22. Lee, D. E., Hwang, I., Valente, C. M., Oliveira, J., & Dornfeld, D. A. (2006). Precision manufacturing process monitoring with acoustic emission. International Journal of Machine Tools and Manufacture, 46(2), 176–188.CrossRefGoogle Scholar
  23. Lee, S. K., Kim, T. R., Lee, S. G., & Park, S. K. (2010). Degradation mechanism of check valves in nuclear power plants. Annals of Nuclear Energy, 37(4), 621–627.CrossRefGoogle Scholar
  24. Lin, Y. H., Lee, W. S., & Wu, C. Y. (2013). A novel signal processing approach for valve health condition classification of a reciprocating compressor with seeded faults considering time-frequency partitions. Journal of Marine Science and Technology, 21(5), 578–585.Google Scholar
  25. Mahamad, A. K., & Hiyama, T. (2011). Fault classification based artificial intelligent methods of induction motor bearing. International Journal Innovative Computing, Information and Control, 7(9), 5477–5494.Google Scholar
  26. Musselman, M., & Djurdjanovic, D. (2012). Time–frequency distributions in the classification of epilepsy from EEG signals. Expert Systems with Applications, 39(13), 11413–11422.CrossRefGoogle Scholar
  27. National Instruments Corporation. (2015). sbRIO-9636 OEM operating instructions and specifications. Accessed 15 August 2015
  28. Papandreou-Suppappola, A. (Ed.). (2002). Applications in time–frequency signal processing (1st ed.). Boca Raton: CRC Press.Google Scholar
  29. Pichler, K., Lughofer, E., Pichler, M., Buchegger, T., Klement, E. P., & Huschenbett, M. (2016). Fault detection in reciprocating compressor valves under varying load conditions. Mechanical Systems and Signal Processing, 70, 104–119.Google Scholar
  30. Raoux, S., Liu, K., Guo, X., & Silvetti, D. (1998). In-situ RF diagnostic for PECVD process control. In Proceedings of the materials research society (MRS), symposium II (Vol. 502, pp. 53–58). Cambridge University Press.Google Scholar
  31. Saxena, A., & Saad, A. (2007). Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing, 7(1), 441–454.CrossRefGoogle Scholar
  32. Shadmehr, R., Angell, D., Chou, P. B., Oehrlein, G. S., & Jaffe, R. S. (1992). Principal component analysis of optical emission spectroscopy and mass spectrometry: Application to reactive ion etch process parameter estimation using neural networks. Journal of the Electrochemical Society, 139(3), 907–914.CrossRefGoogle Scholar
  33. Shen, C. W., Cheng, M. J., & Chen, C. W. (2011). A fuzzy AHP-based fault diagnosis for semiconductor lithography process. International Journal of Innovative Computing, Information and Control, 7(2), 805–815.Google Scholar
  34. Tang, J., Dornfeld, D., Pangrle, S. K., & Dangca, A. (1998). In-process detection of microscratching during CMP using acoustic emission sensing technology. Journal of Electronic Materials, 27(10), 1099–1103.CrossRefGoogle Scholar
  35. Wang, Q. H., Zhang, Y. Y., Cai, L., & Zhu, Y. S. (2009). Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble. Mechanical Systems and Signal Processing, 23(5), 1683–1695.CrossRefGoogle Scholar
  36. Wu, H., Chang, C., Chen, B., Lee, C., Chang, C., Ko, J., Zhou, M., & Liang, M. (2003). Fault detection and classification of plasma CVD tool. In Proceedings of the 2003 IEEE international symposium on semiconductor manufacturing (pp. 123–125).Google Scholar
  37. Wuxing, L., Tse, P. W., Guicai, Z., & Tielin, S. (2004). Classification of gear faults using cumulants and the radial basis function network. Mechanical Systems and Signal Processing, 18(2), 381–389.CrossRefGoogle Scholar
  38. Yang, B. S., Hwang, W. W., Ko, M. H., & Lee, S. J. (2005). Cavitation detection of butterfly valve using support vector machines. Journal of Sound and Vibration, 287(1), 25–43.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Lam Research CorporationFremontUSA
  2. 2.Department of Mechanical EngineeringUniversity of TexasAustinUSA

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